Pricing math, feature coverage, lock in vectors, and the buyer side framework for the next AI platform renewal conversation.
AWS SageMaker, Azure Machine Learning, and Google Vertex AI carry similar feature breadth and diverging commercial profiles. The platform choice is a commercial choice with technical guard rails.
The buyer side discipline is to compare at workload level. Read the related GenAI vendors practice, the AWS services practice, the Google Cloud services practice, and the GenAI knowledge hub.
The three platforms price the core ML services along similar lines. The unit economics diverge at the edges.
| Service | SageMaker | Azure ML | Vertex AI |
|---|---|---|---|
| Notebook (4 vCPU, 16 GB) | $0.46/hr | $0.42/hr | $0.44/hr |
| Training (ml.g5.2xlarge or equivalent) | $1.61/hr | $1.46/hr | $1.52/hr |
| Real time endpoint (ml.m5.xlarge) | $0.230/hr | $0.192/hr | $0.215/hr |
| Batch transform | $0.230/hr | $0.192/hr | $0.215/hr |
| Model registry | Included | Included | Included |
| Pipeline orchestration | $0.10/step | Included | $0.01/step |
The rate cards converge inside fifteen percent at the headline level. The negotiation leverage lives in committed use discounts, family flexibility, and integration with the broader hyperscaler commit envelope. The platform choice is not driven by per service price.
The three platforms cover the same modeling workflow. The feature coverage matrix highlights where each platform differentiates.
Lock in does not live in the modeling code. It lives in the data plane, the pipeline orchestration, and the model registry.
Training and inference carry different commercial profiles. The split shapes the platform choice.
Most enterprise ML estates run the hybrid pattern. Training cost lives in spot capacity and committed GPU pools. Inference cost lives in reserved endpoints. The buyer side discipline separates the two cost lines and negotiates each line on its own discount instrument.
The ML platform negotiation runs through the same five levers as the broader hyperscaler commit conversation.
The eight step checklist below moves the enterprise from platform sprawl to a documented ML commercial posture.
None of the three is meaningfully cheapest at the headline rate card. Per service price points converge inside fifteen percent. The commercial difference lives in the discount band, the commit flexibility, the foundation model economics, and the integration with the broader hyperscaler commit envelope. The cheapest platform is the one negotiated best.
Yes, with discipline. The lock in lives in the data plane, the pipeline orchestration, and the model registry. The modeling code is portable. The buyer side norm is to keep at least one credible training pipeline and one credible inference workload on a second platform. The discipline anchors the alternative for the commit conversation.
Yes. Foundation models change the economics. Bedrock, Azure OpenAI, and Vertex Model Garden each carry distinct pricing and bundling rules. The foundation model spend can exceed the underlying compute and storage spend on heavily prompt driven workloads. The commit conversation should include the foundation model line item explicitly.
The discount lift on ML platform commits runs twelve to twenty six percent when the credible alternative position is documented. The lift depends on the commit scale, the foundation model envelope, the portability of the workloads, and the strategic value the hyperscaler assigns to the account at renewal.
Functionally yes, commercially no. The same modeling workflow runs across all three. The commercial profiles differ. The integration depth into the broader hyperscaler service catalog differs. The platform choice is therefore a commercial choice with technical guard rails, not a pure technology choice.
The buyer side discipline keeps the heavy training and inference workloads on the primary platform, places one credible workload on a second platform as the alternative anchor, and treats foundation model spend as a separate negotiation line. The discipline produces sustained discount lift across every renewal cycle.
Redress runs the ML platform commercial workstream inside the broader hyperscaler renewal cycle. The engagement baselines the ML estate, splits the cost into training, inference, and foundation model lines, maps portability, scores the TCO, and presents the credible alternative inside the commit conversation.
The engagement is independent. Buyer side. Industry Recognized. Five hundred plus enterprise software engagements. Two billion plus in client spend under advisory. Read the related Vendor Shield, the Renewal Program, the Benchmark Program, the Software Spend Assessment, the Benchmarking framework, the about us page, the management team page, the locations page, and the contact page.
A buyer side framework for the AI platform commit conversation. ML platform comparison benchmarks, foundation model economics, training and inference cost split, and the credible alternative play template.
Used across more than five hundred enterprise software engagements. Independent. Buyer side. Built for enterprises running large ML estates across multiple hyperscalers.
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Open the Paper →We split the ML estate into training, inference, and foundation model lines, kept one credible inference workload running on the second platform, and used the documented alternative inside the commit conversation. The platform discount lift landed at twenty one percent and the foundation model envelope came inside the commit.
We have run 500+ enterprise clients across 11 publishers. Every engagement starts with one conversation.
SageMaker, Azure ML, and Vertex AI renewal signals, foundation model economics, training and inference commit signals, and the wider ML platform commercial leverage signals across every renewal cycle.
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